LINC

LINC constructs and analyzes co-expression networks between long intergenic non-coding RNAs (lincRNAs) and protein-coding genes to predict biological contexts using the 'Guilty by Association' principle.

Key Features:

• Co-expression Network Analysis: Constructs and analyzes co-expression networks that map interactions between lincRNAs and protein-coding genes.
• 'Guilty by Association' Inference: Applies the 'Guilty by Association' principle to infer lincRNA function from associations with annotated protein-coding genes.
• Biological Context Prediction: Predicts biological functions and contexts for lincRNAs by associating them with sets of protein-coding genes.
• Statistical Association Methods: Utilizes statistical methods to identify and interpret associations between lincRNAs and protein-coding genes.
• Bioconductor (R) Integration: Integrates with the Bioconductor framework in R for compatibility and interoperability with other genomic analysis tools.

Scientific Applications:

• Functional Annotation of lincRNAs: Infers potential roles and mechanisms of lincRNAs in biological processes based on co-expression with protein-coding genes.
• Integration into Genomic Analysis Workflows: Embeds within Bioconductor-based genomic data analysis workflows to support downstream statistical and functional analyses.

Methodology:

LINC is built on co-expression network theory and utilizes statistical methods to identify and interpret associations between lincRNAs and protein-coding genes, and it integrates with the Bioconductor framework in R.